| Abstract | On-line Analytical Processing (OLAP) techniques commonly used in data warehouses allow the exploration of data cubes according to different analysis axes (dimensions) and different abstraction levels in a dimension hierarchy. However, such techniques are not appropriate for an automatic and efficient mining of multidimensional data. This is mainly due to multidimensionality and the generally large size of data. Since data cubes are nothing but multi-way tables, we propose to analyze the potential of two probabilistic modelling techniques, namely non-negative multi-way factorization and log-linear modelling, for the ultimate objective of compressing and mining aggregate and multidimensional values. For the former, we compute the set of components that best fit the initial data set and whose superposition coincides with the original data, while for the latter we identify a parsimonious model (i.e., one with a reduced set of parameters), highlight strong associations among dimensions and discover possible outliers in data cells. A real life example will be used to (i) discuss the potential benefits of the modelling output on cube exploration and mining, (ii) show how OLAP queries can be answered in an approximate way, and (iii) illustrate the strengths and limitations of these modelling approaches. |
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